Physiological Profile Assessment of Posture in Children and Adolescents with Autism Spectrum Disorder and Typically Developing Peers
Bibliographic record
Abstract
A sound postural system requires sensorimotor integration. Evidence suggests that individuals with Autism Spectrum Disorder (ASD) present sensorimotor integration impairments. The Physiological Profile Assessment (PPA) can be used to evaluate postural capacity assessing five physiological subsets (i.e., vision, reaction time, peripheral sensation, lower limb strength, balance); however, no studies applied the PPA in young individuals. Therefore, this study aimed to investigate the PPA in children and adolescents with ASD compared with age-matched typically developing (TD) individuals and examine the relationship between the PPA subset within the ASD and TD participants according to different age groups. Percentiles from the PPA were obtained from the TD children and adolescents (n = 135) for each test. Performances of the individuals with ASD (n = 18) were examined relative to the TD percentiles. ASD participants’ scores were above the 90th percentile (i.e., poor performance) in most sensory, motor and balance parameters. Performance in most of the PPA tests significantly improved with older age in the TD group but not in the ASD group. The study findings support the use of the PPA in TD children and adolescents while further research should investigate postural capacity in a larger ASD sample to enhance the understanding of sensorimotor systems contributing to compromised postural control.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".